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Results 1 - 10
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1,120
Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions
- Journal of Machine Learning Research
, 2002
"... This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse&ap ..."
Abstract
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Cited by 603 (20 self)
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' framework that we call cluster ensembles. The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information. In addition to a direct maximization approach, we propose three effective and efficient techniques for obtaining high-quality combiners
Bayesian cluster ensembles
- In Proceedings of the 9th SIAM International Conference on Data Mining
, 2009
"... Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of the basic cluster ensemble problem, notably including cluster ensembles with missing values, as well as row-distributed or ..."
Abstract
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Cited by 24 (2 self)
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Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of the basic cluster ensemble problem, notably including cluster ensembles with missing values, as well as row
A Survey of Cluster Ensemble
"... Cluster ensembles are assortment of individual solutions to a certain clustering crisis which are required to consider in a wide sort of applications. This paper gives the general process of the cluster ensemble and overview of different types of consensus function. ..."
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Cluster ensembles are assortment of individual solutions to a certain clustering crisis which are required to consider in a wide sort of applications. This paper gives the general process of the cluster ensemble and overview of different types of consensus function.
A CLUE for CLUster Ensembles
- Journal of Statistical Software
, 2005
"... Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structu ..."
Abstract
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Cited by 34 (7 self)
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Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data
Cluster Ensemble Selection
, 2008
"... This paper studies the ensemble selection problem for unsupervised learning. Given a large library of different clustering solutions, our goal is to select a subset of solutions to form a smaller but better performing cluster ensemble than using all available solutions. We design our ensemble select ..."
Abstract
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Cited by 32 (1 self)
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This paper studies the ensemble selection problem for unsupervised learning. Given a large library of different clustering solutions, our goal is to select a subset of solutions to form a smaller but better performing cluster ensemble than using all available solutions. We design our ensemble
Weighted clustering ensembles
- In Proceedings of The 6th SIAM International Conference on Data Mining
, 2006
"... Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. Cluster ensembles can provide robust and stable solutions by leveraging the consensus across multiple clustering results, while averaging out emergent spurious structures that arise due to the ..."
Abstract
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Cited by 28 (7 self)
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Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. Cluster ensembles can provide robust and stable solutions by leveraging the consensus across multiple clustering results, while averaging out emergent spurious structures that arise due
Adaptive clustering ensembles
- Proc. 17th Int’l Conf. Pattern Recognition
, 2004
"... Clustering ensembles combine multiple partitions of the given data into a single clustering solution of better quality. Inspired by the success of supervised boosting algorithms, we devise an adaptive scheme for integration of multiple non-independent clusterings. Individual partitions in the ensemb ..."
Abstract
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Cited by 22 (1 self)
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Clustering ensembles combine multiple partitions of the given data into a single clustering solution of better quality. Inspired by the success of supervised boosting algorithms, we devise an adaptive scheme for integration of multiple non-independent clusterings. Individual partitions
FAST CONVEGENCE CLUSTERING ENSEMBLE
"... Clustering ensemble combines some clustering outputs to obtain better results. High robustness, accuracy and stability are the most important characteristics of clustering ensembles. Previous clustering ensembles usually use k-means to generate ensemble members. The main problem of k-means is initia ..."
Abstract
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Clustering ensemble combines some clustering outputs to obtain better results. High robustness, accuracy and stability are the most important characteristics of clustering ensembles. Previous clustering ensembles usually use k-means to generate ensemble members. The main problem of k
Results 1 - 10
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1,120
